{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# SEIRS+ Community Testing, Tracing, & Isolation Demo\n",
    "\n",
    "**This notebook provides a demonstration of the functionality of the Extended SEIRS+ Network Model and offers a sandbox for easily changing simulation parameters and scenarios.** \n",
    "For a more thorough walkthrough of the model and use of this package, refer to the README."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Installing and Importing the model code\n",
    "All of the code needed to run the model is imported from the ```models``` module of this package.\n",
    "\n",
    "#### Install the package using ```pip```\n",
    "The package can be installed on your machine by entering this in the command line:\n",
    "\n",
    "```sudo pip install seirsplus```\n",
    "\n",
    "Then, the ```models``` module can be imported into your scripts as shown here:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from seirsplus.models import *\n",
    "import networkx"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### *Alternatively, manually copy the code to your machine*\n",
    "*You can use the model code without installing a package by copying the ```models.py``` module file to a directory on your machine. In this case, the easiest way to use the module is to place your scripts in the same directory as the module, and import the module as shown here:*\n",
    "```python\n",
    "from models import *\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Generating interaction networks\n",
    "\n",
    "This model simulates SEIRS epidemic dynamics for populations with a structured interaction network (as opposed to standard deterministic SIR/SEIR/SEIRS models, which assume uniform mixing of the population). As such, a graph specifying the interaction network for the population must be specified, where each node represents an individual in the population and edges connect individuals who have regular interactions.\n",
    "\n",
    "The interaction network can be specified by a ```networkx``` Graph object or a 2D numpy array representing the adjacency matrix, either of which can be defined and generated by any method.\n",
    "\n",
    "*Here, we use a ```custom_exponential_graph()``` generation function included in this package, which generates power-law graphs that have degree distributions with two exponential tails. For more information on this custom graph type and its generation, see the README.*\n",
    "\n",
    "**_Note:_** *Simulation time increases with network size. Small networks simulate quickly, but have more stochastic volatility. Networks with ~10,000 are large enough to produce per-capita population dynamics that are generally consistent with those of larger networks, but small enough to simulate quickly. We recommend using networks with ~10,000 nodes for prototyping parameters and scenarios, which can then be run on larger networks if more precision is required (for more on this, see README).*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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mERHpGLenp8aPH8+0adOoqanhZz/7GcnJyZ3a0G233UZOTg5Tp06lqamJWbNmccUVV5Cb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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x122074fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "numNodes = 10000\n",
    "baseGraph    = networkx.barabasi_albert_graph(n=numNodes, m=9)\n",
    "# Baseline normal interactions:\n",
    "G_normal     = custom_exponential_graph(baseGraph, scale=100)\n",
    "plot_degree_distn(G_normal, max_degree=40)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Epidemic scenarios of interest often involve interaction networks that change in time. Multiple interaction networks can be defined and used at different times in the model simulation, as will be shown below.\n",
    "\n",
    "*Here we generate a graph representing interactions during corresponding to Social Distancing, where each individual drops some portion of their normal interactions with others. Again, we use the ```custom_exponential_graph()``` to generate this graph; for more information, see the README.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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FBAQEkJ+fz3vvvUdQUBBZWVkMHTq0U+cUEZGu5TE0xo0bx7333kt1dTU/+9nPGD16dKdO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\nRMQ0hYaIiJim0BAREdMUGiIiYtr/AUC4aZXjeyliAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1245fb610>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Social distancing interactions:\n",
    "G_distancing = custom_exponential_graph(baseGraph, scale=10)\n",
    "plot_degree_distn(G_distancing, max_degree=40)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This SEIRS+ model features dynamics corresponding to testing individuals for the disease and moving individuals with detected infection into a state where their rate of recovery, mortality, etc may be different. In addition, given that this model considers individuals in an interaction network, a separate graph defining the interactions for individuals with detected cases can be specified.\n",
    "\n",
    "*Here we generate a graph representing the interactions that individuals have when they test positive for the disease. In this case, a significant portion of each individual's normal interaction edges are removed from the graph, as if the individual is quarantined upon detection of infection. Again, we use the ```custom_exponential_graph()``` to generate this graph; for more information, see the README.*\n",
    "\n",
    "For more information on how testing, contact tracing, and detected cases are handled in this model, see the README."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Lu3bt0uzZsyWd+VCirl27ytfXV9ddd50kKTAwsMHbb4SFhUmSdu7cqeLiYsXH\nx0uS6urqdODAAbvrA9wJhQGY0K1bNxUVFSkqKkrbtm1TXV2dwsLCNH/+fAUHB2vjxo06cuSIfHx8\n9MEHH0iSTpw4oT179tjW8fPbSIeHh6t///5KT0+X1WrV4sWLFRoaand9gDuhMAATRo0apRkzZmjU\nqFEKDw9XmzZtlJaWpqSkJNXV1clisejpp59W165dVVhYqLi4OAUFBalt27Zq06ZNg3XdfPPN2rBh\ng0aPHq1Tp04pKipKfn5+dtcHuBPefBBoRrt27dL27dt15513qry8XEOGDNGqVavk7e3t6mjAeaMw\ngGZ06tQpPf744zp27Jjq6+s1duxYDR8+3NWxgGZBYQAATOHGPQCAKRQGAMAUCgMAYAqFAQAwhcIA\nAJhCYQAATPk/4388ATq3DwQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1245da590>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Quarantine interactions:\n",
    "G_quarantine = custom_exponential_graph(baseGraph, scale=5)\n",
    "plot_degree_distn(G_quarantine, max_degree=40)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Initializing the model parameters\n",
    "All model parameter values, including the normal and quarantine interaction networks, are set in the call to the ```SEIRSNetworkModel``` constructor. The normal interaction network ```G``` and the basic SEIR parameters ```beta```, ```sigma```, and ```gamma``` are the only required arguments. All other arguments represent parameters for optional extended model dynamics; these optional parameters take default values that turn off their corresponding dynamics when not provided in the constructor. For clarity and ease of customization in this notebook, all available model parameters are listed below. \n",
    "\n",
    "For more information on parameter meanings, see the README.\n",
    "\n",
    "*The parameter values shown correspond to rough estimates of parameter values for the COVID-19 epidemic.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = SEIRSNetworkModel(G       =G_normal, \n",
    "                          beta    =0.155, \n",
    "                          sigma   =1/5.2, \n",
    "                          gamma   =1/12.39, \n",
    "                          mu_I    =0.0004,\n",
    "                          mu_0    =0, \n",
    "                          nu      =0, \n",
    "                          xi      =0,\n",
    "                          p       =0.5,\n",
    "                          Q       =G_quarantine, \n",
    "                          beta_D  =0.155, \n",
    "                          sigma_D =1/5.2, \n",
    "                          gamma_D =1/12.39, \n",
    "                          mu_D    =0.0004,\n",
    "                          theta_E =0, \n",
    "                          theta_I =0, \n",
    "                          phi_E   =0, \n",
    "                          phi_I   =0, \n",
    "                          psi_E   =1.0, \n",
    "                          psi_I   =1.0,\n",
    "                          q       =0.5,\n",
    "                          initI   =numNodes/100, \n",
    "                          initE   =0, \n",
    "                          initD_E =0, \n",
    "                          initD_I =0, \n",
    "                          initR   =0, \n",
    "                          initF   =0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Checkpoints\n",
    "Model parameters can be easily changed during a simulation run using checkpoints. A dictionary holds a list of checkpoint times (```checkpoints['t']```) and lists of new values to assign to various model parameters at each checkpoint time. Any model parameter listed in the model constrcutor can be updated in this way. Only model parameters that are included in the checkpoints dictionary have their values updated at the checkpoint times, all other parameters keep their pre-existing values.\n",
    "\n",
    "*The checkpoints shown here correspond to starting social distancing and testing at time ```t=20``` (the graph ```G``` is updated to ```G_distancing``` and locality parameter ```p``` is decreased to ```0.1```; testing params ```theta_E```, ```theta_I```, ```phi```, and ```phi_I``` are set to non-zero values) and then stopping social distancing at time ```t=100``` (```G``` and ```p``` changed back to their \"normal\" values; testing params remain non-zero).*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "checkpoints = {'t':       [20, 100], \n",
    "               'G':       [G_distancing, G_normal], \n",
    "               'p':       [0.1, 0.5], \n",
    "               'theta_E': [0.02, 0.02], \n",
    "               'theta_I': [0.02, 0.02], \n",
    "               'phi_E':   [0.2, 0.2], \n",
    "               'phi_I':   [0.2, 0.2]}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Running the simulation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "t = 0.00\n",
      "t = 10.01\n",
      "[Checkpoint: Updating parameters]\n",
      "t = 20.01\n",
      "t = 30.01\n",
      "t = 40.02\n",
      "t = 50.04\n",
      "t = 60.01\n",
      "t = 70.06\n",
      "t = 80.05\n",
      "t = 90.04\n",
      "[Checkpoint: Updating parameters]\n",
      "t = 100.00\n",
      "t = 110.03\n",
      "t = 120.01\n",
      "t = 130.05\n",
      "t = 140.03\n",
      "t = 150.00\n",
      "t = 160.03\n",
      "t = 170.00\n",
      "t = 180.05\n",
      "t = 190.01\n",
      "t = 200.00\n",
      "t = 210.07\n",
      "t = 220.01\n",
      "t = 230.00\n",
      "t = 240.04\n",
      "t = 250.04\n",
      "t = 260.01\n",
      "t = 270.01\n",
      "t = 280.03\n",
      "t = 290.02\n",
      "t = 300.01\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.run(T=300, checkpoints=checkpoints)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualizing the results\n",
    "The ```SEIRSNetworkModel``` class has a ```plot()``` convenience function for plotting simulation results on a matplotlib axis. This function generates a line plot of the frequency of each model state in the population by default, but there are many optional arguments that can be used to customize the plot.\n",
    "\n",
    "The ```SEIRSNetworkModel``` class also has convenience functions for generating a full figure out of model simulation results (optionaly arguments can be provided to customize the plots generated by these functions). \n",
    "- ```figure_basic()``` calls the ```plot()``` function with default parameters to generate a line plot of the frequency of each state in the population.\n",
    "- ```figure_infections()``` calls the ```plot()``` function with default parameters to generate a stacked area plot of the frequency of only the infection states ($E$, $I$, $D_E$, $D_I$) in the population.\n",
    "\n",
    "For more information on the built-in plotting functions, see the README."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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3tjkJEBy6DACAeQ7DMAy7Q1RXWlqaMjMz7Y5hmtfrlSRFRETYnAQIDl0GAMA8VrgtxHCC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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1245ff9d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model.figure_infections(vlines=checkpoints['t'], ylim=0.15)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Reference simulation visualizations\n",
    "\n",
    "We can also visualize the results of other simulation(s) as a reference for comparison of our main simulation.\n",
    "\n",
    "Here we simulate a model where no distancing or testing takes place, so that we can compare the effects of these interventions:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "t = 0.02\n",
      "t = 10.00\n",
      "t = 20.04\n",
      "t = 30.01\n",
      "t = 40.01\n",
      "t = 50.00\n",
      "t = 60.00\n",
      "t = 70.00\n",
      "t = 80.00\n",
      "t = 90.01\n",
      "t = 100.00\n",
      "t = 110.00\n",
      "t = 120.00\n",
      "t = 130.01\n",
      "t = 140.01\n",
      "t = 150.01\n",
      "t = 160.01\n",
      "t = 170.00\n",
      "t = 180.00\n",
      "t = 190.00\n",
      "t = 200.09\n",
      "t = 210.20\n",
      "t = 220.23\n",
      "t = 230.43\n",
      "t = 240.30\n",
      "t = 250.18\n",
      "t = 260.34\n",
      "t = 280.54\n",
      "t = 300.59\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ref_model = SEIRSNetworkModel(G=G_normal, beta=0.155, sigma=1/5.2, gamma=1/12.39, mu_I=0.0004, p=0.5,\n",
    "                          Q=G_quarantine, beta_D=0.155, sigma_D=1/5.2, gamma_D=1/12.39, mu_D=0.0004,\n",
    "                          theta_E=0, theta_I=0, phi_E=0, phi_I=0, psi_E=1.0, psi_I=1.0, q=0.5,\n",
    "                          initI=numNodes/100)\n",
    "ref_model.run(T=300)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can visualize our main simulation together with this reference simulation by passing the model object of the reference simulation to the appropriate figure function argument (note: a second reference simulation could also be visualized by passing it to the ```dashed_reference_results``` argument):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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jRJx++um2XYuC20bhVbpAqmMsAwAQPQpuG40ePdp0BCAuGMsAAETPloNv0KaqqkpVVVWm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\n7sm7kWMQQmAdPIxaGkQrH19tBgB7IELqpe34rzj/yFjpDP3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iKApWdz/moXY8a5dMSZq+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Pfee1x33XVorXn00UcBqKiooLm5mRkzZuA4DtXV1dx6663cddddJytpQgghhBBCnDAn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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1246bf810>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model.figure_infections(vlines=checkpoints['t'], ylim=0.2, shaded_reference_results=ref_model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As further demonstration, we might also wish to compare the results of these network model simulations to a deterministic model simulation of the same SEIRS parameters (with no interventions in this case):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "t = 299.90\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ref_model_determ = SEIRSModel(beta=0.147, sigma=1/5.2, gamma=1/12.39, mu_I=0.0004, initI=100, initN=10000) \n",
    "ref_model_determ.run(T=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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WFqKwsBCjR49GYWFhRLEcenyo4C4rK8PKlSvlTRpifX5LWxgLggCd7j9zmVlZWQAArVYL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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1246bf110>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model.figure_infections(vlines=checkpoints['t'], ylim=0.2, \n",
    "                        shaded_reference_results=ref_model, shaded_reference_label='network: no interventions',\n",
    "                        dashed_reference_results=ref_model_determ, dashed_reference_label='deterministic: no interventions')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
